Cells adapt to their environment largely through the activities of signal transduction networks. Aberrations of normal signaling networks can lead to human diseases such as cancer and diabetes. Transforming Growth Factor-_ (TGF-_) is a prominent signaling pathway that regulates diverse aspects of cellular homeostasis including proliferation, differentiation, migration, and death. How a single cytokine like TGF-_ can exert such diverse biological effects in a cell context- dependent manner is an outstanding question in biology. While it is clear that TGF-_ signals through the intracellular mediator Smad proteins to regulate gene expression, relatively little is known about how cells respond to different ligand doses and how variations in ligand exposure impact Smad signaling dynamics and subsequent gene expression. Our long-term goal is to predict cellular responses to TGF-_ signaling based on molecular mechanisms. The objective of this application is to quantitatively assess Smad signaling dynamics and develop a comprehensive mathematical model that is able to predict systems-level ligand dose-dependent Smad signaling dynamics. We hypothesize the following principles of TGF-_ signal transduction, upon which we have configured the proposal: 1) Cells decode the ligand dose (TGF-_ molecules per cell) through a T_RII receptor trafficking-dependent mechanism, 2) Cells transduce the signal inside the cell by setting the rates of R-Smad phosphorylation relative to the rate of dephosphorylation, and 3) Smad oligomerization fine-tunes the signal dynamic properties and serves as a mechanism for signal specificity and target diversity. Our proposal evaluates the contribution of the diverse events in TGF-_ signaling to determining the overall signal, which in turn determines the resulting gene expression profile and biological response. We will investigate our hypothesis using a systems biology approach that integrates kinetic experiments and mathematical modeling, as described in the following specific aims:1) Determine the mechanism by which cells decode the TGF-_ ligand dose. 2) Determine how the rates of R-Smad phosphorylation and dephosphorylation regulate Smad signal transduction. 3) Evaluate the dynamic properties of Smad oligomerization. TGF-_ signaling is a dynamic process that operates in the context of global cellular regulatory network. The system properties and quantitative aspects of this network are poorly defined. We developed an initial mathematical model for TGF-_/Smad signaling and we are well positioned to verify these predictions and the model assumptions through experiment and further modeling analysis. We expect that applying the innovative systems biology approach to study TGF-_/Smad signaling will fundamentally advance our knowledge in this major signaling network. In particular, we foresee using this model to predict biological responses to TGF-_ in health and disease. Given that the TGF-_ signal transduction pathway is frequently targeted for aberrations in human cancer cells, a quantitative understanding of the pathway will be essential for evaluating the efficacy of antitumor drugs and mitigating undesirable side effects in therapeutic interventions. PUBLIC HEALTH RELEVANCE: Transforming Growth Factor-_ (TGF-_) is a prominent signaling pathway that regulates diverse aspects of cellular homeostasis including proliferation, differentiation, migration, and death. The objective of this application is to quantitatively assess TGF-_ signaling dynamics and develop a comprehensive mathematical model that is able to predict biological responses to TGF-_ in health and disease. Given that the TGF-_ signal transduction pathway is frequently targeted for aberrations in human cancer cells, a quantitative understanding of the pathway will be essential for evaluating the efficacy of antitumor drugs and mitigating undesirable side effects in therapeutic interventions.